People Prime Worldwide
Website:
people-prime.com
Job details:
About Company : Our Client is a leading Indian multinational IT services and consulting firm. It provides digital transformation, cloud computing, data analytics, enterprise application integration, infrastructure management, and application development services. The company caters to over 700 clients across industries such as banking and financial services, manufacturing, technology, media, retail, and travel & hospitality.
Its industry-specific solutions are designed to address complex business challenges by combining domain expertise with deep technical capabilities. With a global workforce of over 80,000 professionals and a presence in more than 50 countries.
Job Title: Machine Learning Engineer
Locations: Bengaluru, Karnataka, India.
Experience: 5-10 Years (Relevant)
Employment Type: Contract to Hire
Work Mode : Work From Office
Notice Period : Immediate to 15 Days
Job Description:
- Design and implement end‑to‑end MLOps pipelines (data ingestion → training → validation → deployment → monitoring)
- Productionize ML models using CI/CD for ML
- Build and manage model versioning, experiment tracking, and artifact management
- Deploy models using REST APIs, batch jobs, or streaming pipelines
- Monitor model performance, data drift, concept drift, and system health
- Automate retraining, rollback, and promotion workflows
- Ensure model governance, compliance, and auditability
- Collaborate with Data Scientists, Platform, Cloud, and DevOps teams
- Optimize infrastructure cost, scalability, and reliabilit
- Operationalize object detection, segmentation, OCR, and video analytics models
- Automate dataset versioning, annotation pipeline integration, and model retraining
- Design and manage end‑to‑end CV MLOps pipelines
- (image/video ingestion → preprocessing → training → validation → deployment → monitoring)
- Operationalize object detection, segmentation, OCR, and video analytics models
- Automate dataset versioning, annotation pipeline integration, and model retraining
- Implement CI/CD for CV models with GPU‑aware workflows
- Deploy models for:
- Real‑time inference (APIs, streaming)
- Batch inference (offline processing)
- Edge/IoT devices
- Monitor model accuracy, drift (data + concept), latency, and resource usage
- Enable model rollback, shadow deployment, A/B testing
- Ensure enterprise governance: traceability, approvals, documentation
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